Subject description - BE4M33SSU
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BE4M33SSU | Statistical Machine Learning | Extent of teaching: | 2P+2C | ||
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Guarantors: | Flach B. | Roles: | PO,PV,V | Language of teaching: | EN |
Teachers: | Drchal J., Flach B., Franc V. | Completion: | Z,ZK | ||
Responsible Department: | 13133 | Credits: | 6 | Semester: | Z |
Anotation:
The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve tasks given a set of examples and some prior knowledge about the task. This includes typical tasks in speech and image recognition. The course has the following two main objectives1. | to present fundamental learning concepts such as risk minimisation, maximum likelihood estimation and Bayesian learning including their theoretical aspects, | |
2. | to consider important state-of-the-art models for classification and regression and to show how they can be learned by those concepts. |
Study targets:
The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve tasks given a set of examples and some prior knowledge about the task.Course outlines:
The course will cover the following topics - Empirical risk minimization, consistency, bounds - Kernel SVMs, RKHS, regression - Semi-supervised learning - Unsupervised learning, EM algorithm, mixture models - Bayesian learning - Deep (convolutional) networks and Boltzmann machines (graphical models) - Supervised learning for deep networks - Hopfield nets and energy minimisation (MAP in MRFs) - Structured output SVMs - Sampling methods, sampling from models - Ensemble learning, random forestsExercises outline:
Labs will be dedicated to practical implementations of selected methods discussed in the course as well as seminar classes with task-oriented assignments.Literature:
1. | M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 | |
2. | K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 | |
3. | T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 |
Requirements:
Prerequisites of the course are: - foundations of probability theory and statistics comparable to the scope of the course "Probability, statistics and information theory" (A0B01PSI), - knowledge of statistical decision theory foundations, canonical and advanced classifiers as well as basics of machine learning comparable to the scope of the course "Pattern Recognition and Machine Learning" (AE4B33RPZ)Webpage:
http://cw.fel.cvut.cz/wiki/courses/be4m33ssu/startKeywords:
machine learing, statistical learning Subject is included into these academic programs:Page updated 13.12.2019 17:52:09, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |